Subtraction of aligned images is a means to assess changes in a wide variety of clinical applications. In this paper we explore the information theoretical origin of Mutual Information (MI), which is based on Shannon's entropy.However, the interpretation of standard MI registration as a communication channel suggests that MI is too restrictive a criterion. In this paper the concept of Mutual Information (MI) is extended to (Normalized) Focussed Mutual Information (FMI) to incorporate prior knowledge to overcome some shortcomings of MI. We use this to develop new methodologies to successfully address specific registration problems, the follow-up of dental restorations, cephalometry, and the monitoring of implants.
Deformable image registration with deep network priors: a study on longitudinal PET images
Ecole Centrale de Nantes（南特中央理工学院）；Keosys Medical Imaging（Keosys医学影像公司）；University of Nantes（南特大学）；University of Angers（昂热大学）；ICO Cancer Center（ICO癌症中心）
FusionRegister: Every Infrared and Visible Image Fusion Deserves Registration
School of Artificial Intelligence and Computer Science, Jiangnan University, Wuxi, China（江南大学人工智能与计算机科学学院）；School of Electronic and Information Engineering, Suzhou University of Science and Technology, Suzhou, China（苏州科技大学电子与信息工程学院）
Harvard Medical School（哈佛医学院）；INRIA Sophia Antipolis-Méditerranée（法国国家信息与自动化研究所索菲亚-安提波利斯-地中海中心）；University of North Carolina（北卡罗来纳大学）；Massachusetts Institute of Technology（麻省理工学院）
Stroke Extraction of Chinese Character Based on Deep Structure Deformable Image Registration
Institute of Automation, Chinese Academy of Sciences（中国科学院自动化研究所）；School of Artificial Intelligence, University of Chinese Academy of Sciences（中国科学院大学人工智能学院）
Multimodal Remote Sensing Image Registration with Accuracy Estimation at Local and Global Scales
National Aerospace University（国立航空航天大学）；University of Rennes 1（雷恩第一大学）
本文主要研究遥感图像配准的潜在精度问题。我们研究如何在没有地面实况的情况下估计这种精度，并用于提高单模态和多模态图像对的配准质量。在局部尺度的图像片段，Cramer-Rao下界（CRLB）的配准误差估计的每个局部对应之间的粗配准对的图像。该CRLB由局部图像纹理和噪声属性定义。与仅在配准过程的输出处评估配准精度的标准方法相反，我们将这些有价值的信息用作额外的输入知识。这对发现和剔除异常值以及改进几何变换模型参数的估计有很大帮助。基于这些思想，提出了一种新的基于区域的配准方法RAE（Registration with Accuracy Estimation）。除了其以高精度自动配准非常复杂的多模态图像对的能力之外，RAE方法在全局尺度上提供配准精度作为几何变换模型参数的估计误差的协方差矩阵或作为逐点配准标准偏差。这种精度不依赖于任何地面实况可用性，并单独表征每对配准图像。因此，RAE方法可以识别保证预定义配准精度的图像区域。RAE方法被证明是成功的，达到亚像素精度，同时注册8个复杂的单/多模态和多时相图像对，包括光学光学，光学雷达，光学数字高程模型（DEM）图像和DEM雷达的情况下。在比较中采用的其他方法不能以稳定的方式提供相同测试用例的准确结果。
Unsupervised learning of spatially varying regularization for diffeomorphic image registration
Department of Radiology and Radiological Science, Johns Hopkins School of Medicine, MD, USA（放射科与放射科学系，约翰霍普金斯医学院，马里兰州，美国）；Image Analysis and Communications Laboratory, Department of Electrical and Computer Engineering, Johns Hopkins University, MD, USA（图像分析与通信实验室，电气与计算机工程系，约翰霍普金斯大学，马里兰州，美国）；Department of Electrical and Computer Engineering, Vanderbilt University, TN, USA（电气与计算机工程系，范德比尔特大学，田纳西州，美国）；NVIDIA Corporation, Santa Clara, CA, USA（英伟达公司，圣克拉拉，加利福尼亚州，美国）
A Robust Multimodal Remote Sensing Image Registration Method and System Using Steerable Filters with First- and Second-order Gradients
Southwest Jiaotong University（西南交通大学）；Beijing Institute of Technology（北京理工学院）；State Key Laboratory of Information Engineering in Surveying, Mapping and Remote Sensing（测绘遥感信息工程国家重点实验室）
A Robust and Interpretable Deep Learning Framework for Multi-modal Registration via Keypoints
Cornell University（康奈尔大学）；Cornell Tech（康奈尔理工学院）；Weill Cornell Medical School（威尔康奈尔医学院）；Iterative Scopes（Iterative Scopes公司）；Massachusetts Institute of Technology（麻省理工学院）；Massachusetts General Hospital（麻省总医院）
School of Artificial Intelligence and Robotics, Hunan University（人工智能与机器人学院，湖南大学）；Cornell University（康奈尔大学）；Department of Electrical and Computer Engineering, Johns Hopkins University（电气与计算机工程系，约翰霍普金斯大学）；Department of Electrical and Computer Engineering, National University of Singapore（电气与计算机工程系，国立新加坡大学）
Astroalign: A Python module for astronomical image registration
University of Texas Rio Grande Valley（德克萨斯大学里奥格兰德河谷分校）；Centro Internacional Franco Argentino de Ciencias de la Información y de Sistemas（法阿国际信息科学与系统中心）；Instituto de Astronomía Teórica y Experimental - Observatorio Astronómico Córdoba（理论与实验天文学研究所-科尔多瓦天文台）；Duke University（杜克大学）
Progressive Retinal Image Registration via Global and Local Deformable Transformations
School of Computer Science Wuhan University Wuhan, China；School of Computer Science The University of Sydney Sydney, Australia；School of Computer Science Wuhan University Wuhan, China yuliang\；Institute for Infocomm Research A STAR Singapore cheng\
3D deformable registration of longitudinal abdominopelvic CT images using unsupervised deep learning
University of Cambridge（剑桥大学）；Cancer Research UK Cambridge Centre（英国癌症研究中心剑桥中心）；Medical University Vienna（维也纳医科大学）；Cambridge University Hospitals National Health Service (NHS) Foundation Trust（剑桥大学医院国民保健服务基金会信托）；Eindhoven University of Technology（埃因霍温理工大学）；Leiden University（莱顿大学）；University College London（伦敦大学学院）；Cancer Research UK Cambridge Institute（英国癌症研究中心剑桥研究所）
Disentangling Progress in Medical Image Registration: Beyond Trend-Driven Architectures towards Domain-Specific Strategies
Technical University of Munich（慕尼黑技术大学）；Munich Center for Machine Learning (MCML)（慕尼黑机器学习中心）；Imperial College London（伦敦帝国理工学院）；University of Pennsylvania（宾夕法尼亚大学）；National University of Singapore（新加坡国立大学）
Spatially Covariant Image Registration with Text Prompts
College of Electrical and Information Engineering, Hunan University（湖南大学电气与信息工程学院）；Cornell University（康奈尔大学）；University of Pennsylvania（宾夕法尼亚大学）；New York University（纽约大学）；Shanghai Jiao Tong University（上海交通大学）
A coarse-to-fine framework for unsupervised multi-contrast MR image deformable registration with dual consistency constraint
Paul C. Lauterbur Research Center for Biomedical Imaging, Shenzhen Institutes of Advanced Technology, Chinese Academy of Sciences（中国科学院深圳先进技术研究院保罗·C·劳特伯生物医学成像研究中心）；Pengcheng Laboratory（鹏城实验室）；Pazhou Lab（琶洲实验室）；Guizhou Provincial People’s Hospital（贵州省人民医院）
Collaborative Quantization Embeddings for Intra-Subject Prostate MR Image Registration
University College London（伦敦大学学院）；University of Oxford（牛津大学）；University College London Hospital NHS Foundation Trust（伦敦大学学院医院国民保健服务基金会信托）；Stanford University（斯坦福大学）
Quasiconformal model with CNN features for large deformation image registration
Georgia Institute of Technology（佐治亚理工学院）；Massachusetts Institute of Technology（麻省理工学院）；National Institute of Mental Health（美国国家精神卫生研究所）；The Chinese University of Hong Kong（香港中文大学）
Search-MIND: Training-Free Multi-Modal Medical Image Registration
Lab for Uncertainty in Data and Decision Making (LUCID), Nottingham Biomedical Research Centre (BRC), School of Medicine, University of Nottingham, UK（英国诺丁汉大学医学院诺丁汉生物医学研究中心（BRC）数据与决策不确定性实验室（LUCID））
AutoFuse: Automatic Fusion Networks for Deformable Medical Image Registration
School of Computer Science, The University of Sydney（悉尼大学计算机科学学院）；Institute of Translational Medicine, Shanghai Jiao Tong University（上海交通大学转化医学研究院）；Royal Prince Alfred Hospital（皇家阿尔弗雷德王子医院）
MORPH-LER: Log-Euclidean Regularization for Population-Aware Image Registration
Karlert School of Computing, University of Utah（犹他大学卡尔尔特计算学院）；Scientific Computing and Imaging Institute, University of Utah（犹他大学科学计算与成像研究所）；Biomedical Engineering Department, University of Utah（犹他大学生物医学工程系）
DINE: Distance Is Not Enough -- Learning Global Deformation Priors for Robust Soft-Tissue Point Cloud Registration
Mannheim Institute for Intelligent Systems in Medicine (MIISM), Medical Faculty Mannheim, Heidelberg University（曼海姆医学智能系统研究所（MIISM），海德堡大学曼海姆医学院）；Department of Radiation Oncology, Brigham and Women’s Hospital, Dana-Farber Cancer Institute, Harvard Medical School（布莱根妇女医院放射肿瘤学系，达纳-法伯癌症研究所，哈佛医学院）；Department of Otorhinolaryngology, Head and Neck Surgery, University Medical Center Mannheim, Medical Faculty Mannheim, Heidelberg University（海德堡大学曼海姆医学院曼海姆大学医学中心耳鼻咽喉头颈外科）；Department of Otolaryngology, Head and Neck Surgery, Campus Klinikum Bielefeld Mitte, University Hospital OWL of Bielefeld University（比勒费尔德大学OWL大学医院比勒费尔德市中心校区耳鼻咽喉头颈外科）；Interdisciplinary Center for Scientific Computing (IWR), Heidelberg University（海德堡大学跨学科科学计算中心（IWR））；Central Institute for Computer Engineering (ZITI), Heidelberg University（海德堡大学中央计算机工程研究所（ZITI））；CZS Heidelberg Center for Model-Based AI, Heidelberg University（海德堡大学CZS基于模型的人工智能中心）
